Hierarchical structure of stream ecosystems: consequences for bioassessment
Bibliographic record
Abstract
It has long been recognized that communities and their ecosystems are structured at several, nested spatial scales. But identifying the appropriate scale(s) to collect, analyse and interpret data to answer specific questions about ecosystems has been a vexing problem for ecologists. We collected observations of the benthic invertebrate community and its environment in 10 primarily agricultural tributary streams of the Thames River in southwestern Ontario, Canada. Within each stream we sampled two reaches, in each reach we sampled three riffles, and in each riffle we took three kick samples of invertebrates and characterized the substrate environment. We also characterized the habitat at each of the 20 reaches (10 streams × 2 reaches/stream). Most of the variability in the stream invertebrate community structure (as described with taxonomic richness and the biotic index of tolerance, as well as by the Bray-Curtis distance of the community composition from the mean at a spatial scale) was at larger spatial scales of among streams and between riffles. Much of the substrate and habitat variation was also at the larger spatial scales, as were correlations between the biota and the environment of the benthic invertebrate community. We concluded that for the purposes of bioassessment, characterization of one reach per stream is sufficient, at least in this context, for describing a stream and evaluating its health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".